MGDrivE 3: A decoupled vector-human framework for epidemiological simulation of mosquito genetic control tools and their surveillance

Abstract

Novel mosquito genetic control tools, such as CRISPR-based gene drives, hold great promise in reducing the global burden of vector-borne diseases. As these technologies advance through the research and development pipeline, there is a growing need for modeling frameworks incorporating increasing levels of entomological and epidemiological detail in order to address questions regarding logistics and biosafety. Epidemiological predictions are becoming increasingly relevant to the development of target product profiles and the design of field trials and interventions, while entomological surveillance is becoming increasingly important to regulation and biosafety. We present MGDrivE 3 (Mosquito Gene Drive Explorer 3), a new version of a previously-developed framework, MGDrivE 2, that investigates the spatial population dynamics of mosquito genetic control systems and their epidemiological implications. The new framework incorporates three major developments: i) a decoupled sampling algorithm allowing the vector portion of the MGDrivE framework to be paired with a more detailed epidemiological framework, ii) a version of the Imperial College London malaria transmission model, which incorporates age structure, various forms of immunity, and human and vector interventions, and iii) a surveillance module that tracks mosquitoes captured by traps throughout the simulation. Example MGDrivE 3 simulations are presented demonstrating the application of the framework to a CRISPR-based homing gene drive linked to dual disease-refractory genes and their potential to interrupt local malaria transmission. Simulations are also presented demonstrating surveillance of such a system by a network of mosquito traps. MGDrivE 3 is freely available as an open-source R package on CRAN (https://cran.r-project.org/package=MGDrivE2) (version 2.1.0), and extensive examples and vignettes are provided. We intend the software to aid in understanding of human health impacts and biosafety of mosquito genetic control tools, and continue to iterate per feedback from the genetic control community. Author summaryVector-borne diseases such as malaria cause massive morbidity and mortality throughout much of the world. Currently-available control measures, such as insecticide-based tools and antimalarial drugs, have limited impact and are waning in effectiveness, hence there is a need for novel tools to complement existing ones. Mosquito genetic control tools, such as gene drive systems and genetic versions of the sterile insect technique, offer a range of promising options, the development of which has greatly expanded since the advent of CRISPR-based gene-editing. Recently, we proposed MGDrivE 2 (Mosquito Gene Drive Explorer 2), which incorporates epidemiology into simulations of the dynamics of these systems in spatially-structured mosquito populations; however, that framework relied on simple model representations of vector-borne diseases. Here, we present MGDrivE 3, which decouples the vector portion of the model from the human portion, allowing the mosquito genetic control framework to be paired with more-detailed epidemiological frameworks. As an example, we implement the human transmission dynamics of the Imperial College London malaria model. We also incorporate a network of mosquito traps for surveillance. As genetic control technology edges closer towards field implementation, more detailed predictions of its epidemiological and biosafety implications are needed. We propose MGDrivE 3 to fulfill this role.